Decision Support for Comorbid Conditions via Execution-Time Integration of Clinical Guidelines Using Transaction-based Semantics and Temporal Planning

Author(s):  
William Van Woensel ◽  
Syed Sibte Raza Abidi ◽  
Samina Raza Abidi
Author(s):  
Jennifer R. Marin ◽  
Jonathan Rodean ◽  
Rebekah C. Mannix ◽  
Matt Hall ◽  
Elizabeth R. Alpern ◽  
...  

2014 ◽  
Vol 8 (2) ◽  
pp. 238-246 ◽  
Author(s):  
Gema García-Sáez ◽  
Mercedes Rigla ◽  
Iñaki Martínez-Sarriegui ◽  
Erez Shalom ◽  
Mor Peleg ◽  
...  

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e24043-e24043
Author(s):  
Yazan Rizeq ◽  
Casey Brackett ◽  
Rawan Yousef ◽  
Ferdynand Hebal ◽  
Susan Budds ◽  
...  

e24043 Background: Oncology practices face multiple challenges when trying to implement the Commission on Cancer (CoC) Survivorship Care Plan (SCP) documentation standard. First, it is difficult to systematically identify patients who qualify for a SCP from the electronic medical record (EMR). Second, SCP documentation must be completed within a specific timeframe that does not always align with clinical encounters for routine care. Finally, as practices participate in more regulatory programs, such as the Oncology Care Model (OCM), the burden of meeting the unique but often overlapping documentation standards becomes burdensome. Our goal was to improve adherence to SCP documentation through enhanced clinical documentation workflows and clinical decision support. Methods: To meet CoC SCP standards, SCP templates were built into the EPIC EMR at a single multi-centered institution. These templates allowed the user to indicate on the problem list if the SCP was needed. Likewise, Institute of Medicine (IOM) care plan documentation templates and reminders were built to meet OCM standards. Population dashboards were developed in Tableau to track SCP documentation adherence and provide support regarding eligible patients who required SCP documentation. Patients were eligible who had a cancer diagnosis codes on the problem list, and MC in the EMR. Patients greater than 12 months from being marked curative were considered eligible to complete SCP. Results: Between 2015-2019, 2763 cancer patients were MC by 31 providers, where 806 (30%) were OCM patients. Overall, 1372 (70%) non-OCM patients required an SCP, where 563 (41%) were completed. When examining the OCM patients, 689 (85%) required a SCP, where 552 (80%) were completed. For patients MC in 2018, 163/439 (37%) and 137/190 (72%) of patients received a SCP for the non-OCM and OCM group respectively. For patient MC in 2019 after implementation of the decision support dashboard, 115/145 (79%) and 144/148 (97%) of eligible patients had completed SCP for the non-OCM and OCM group respectively (Table). Conclusions: During the study period, adherence to SCP documentation doubled concordant with CoC standards at that time. Integration of IOM and SCP documentation improved adherence among OMC compared to non-OCM patients. The decision support dashboard helped prioritize cases for SCP documentation for care teams. This is an example of a decision support framework that could be used for other standards driven documentation requirements in cancer.


Author(s):  
Leonardo Lezcano ◽  
Miguel-Ángel Sicilia ◽  
Eydel Rivero

Achieving semantic interoperability between heterogeneous healthcare systems and integrating clinical guidelines in the automatic decision support of healthcare institutions are two key priorities of current medical informatics. They can lead to a significant improvement on patient safety by reducing medical risks and delays in diagnosis, facilitating continuity of care and preventing life threatening adverse events. The present chapter describes a project that addresses those two priorities in the field of Breast Cancer for which effective clinical guidelines are available, as well as the clinical data to apply them. However, the deployment of semantic interoperability techniques based on clinical terminologies such as SNOMED-CT and EHR exchange models such as openEHR and HL7 is required to meaningfully combine the available data. Then data mining techniques are capable of automatically adapting the parameters of clinical guidelines to the particular conditions of each healthcare environment.


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